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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Multi-Source Distilling Domain Adaptation

February 1, 2023

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Authors

Sicheng Zhao

University of California, Berkeley


Guangzhi Wang

Didi Chuxing


Shanghang Zhang

University of California, Berkeley


Yang Gu

Didi Chuxing


Yaxian Li

Didi Chuxing & Renmin University of China


Zhichao Song

Didi Chuxing


Pengfei Xu

Didi Chuxing


Runbo Hu

Didi Chuxing


Hua Chai

Didi Chuxing


Kurt Keutzer

University of California, Berkeley


DOI:

10.1609/aaai.v34i07.6997


Abstract:

Deep neural networks suffer from performance decay when there is domain shift between the labeled source domain and unlabeled target domain, which motivates the research on domain adaptation (DA). Conventional DA methods usually assume that the labeled data is sampled from a single source distribution. However, in practice, labeled data may be collected from multiple sources, while naive application of the single-source DA algorithms may lead to suboptimal solutions. In this paper, we propose a novel multi-source distilling domain adaptation (MDDA) network, which not only considers the different distances among multiple sources and the target, but also investigates the different similarities of the source samples to the target ones. Specifically, the proposed MDDA includes four stages: (1) pre-train the source classifiers separately using the training data from each source; (2) adversarially map the target into the feature space of each source respectively by minimizing the empirical Wasserstein distance between source and target; (3) select the source training samples that are closer to the target to fine-tune the source classifiers; and (4) classify each encoded target feature by corresponding source classifier, and aggregate different predictions using respective domain weight, which corresponds to the discrepancy between each source and target. Extensive experiments are conducted on public DA benchmarks, and the results demonstrate that the proposed MDDA significantly outperforms the state-of-the-art approaches. Our source code is released at: https://github.com/daoyuan98/MDDA.

Topics: AAAI

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HOW TO CITE:

Sicheng Zhao||Guangzhi Wang||Shanghang Zhang||Yang Gu||Yaxian Li||Zhichao Song||Pengfei Xu||Runbo Hu||Hua Chai||Kurt Keutzer Multi-Source Distilling Domain Adaptation Proceedings of the AAAI Conference on Artificial Intelligence (2020) 12975-12983.

Sicheng Zhao||Guangzhi Wang||Shanghang Zhang||Yang Gu||Yaxian Li||Zhichao Song||Pengfei Xu||Runbo Hu||Hua Chai||Kurt Keutzer Multi-Source Distilling Domain Adaptation AAAI 2020, 12975-12983.

Sicheng Zhao||Guangzhi Wang||Shanghang Zhang||Yang Gu||Yaxian Li||Zhichao Song||Pengfei Xu||Runbo Hu||Hua Chai||Kurt Keutzer (2020). Multi-Source Distilling Domain Adaptation. Proceedings of the AAAI Conference on Artificial Intelligence, 12975-12983.

Sicheng Zhao||Guangzhi Wang||Shanghang Zhang||Yang Gu||Yaxian Li||Zhichao Song||Pengfei Xu||Runbo Hu||Hua Chai||Kurt Keutzer. Multi-Source Distilling Domain Adaptation. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.12975-12983.

Sicheng Zhao||Guangzhi Wang||Shanghang Zhang||Yang Gu||Yaxian Li||Zhichao Song||Pengfei Xu||Runbo Hu||Hua Chai||Kurt Keutzer. 2020. Multi-Source Distilling Domain Adaptation. "Proceedings of the AAAI Conference on Artificial Intelligence". 12975-12983.

Sicheng Zhao||Guangzhi Wang||Shanghang Zhang||Yang Gu||Yaxian Li||Zhichao Song||Pengfei Xu||Runbo Hu||Hua Chai||Kurt Keutzer. (2020) "Multi-Source Distilling Domain Adaptation", Proceedings of the AAAI Conference on Artificial Intelligence, p.12975-12983

Sicheng Zhao||Guangzhi Wang||Shanghang Zhang||Yang Gu||Yaxian Li||Zhichao Song||Pengfei Xu||Runbo Hu||Hua Chai||Kurt Keutzer, "Multi-Source Distilling Domain Adaptation", AAAI, p.12975-12983, 2020.

Sicheng Zhao||Guangzhi Wang||Shanghang Zhang||Yang Gu||Yaxian Li||Zhichao Song||Pengfei Xu||Runbo Hu||Hua Chai||Kurt Keutzer. "Multi-Source Distilling Domain Adaptation". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.12975-12983.

Sicheng Zhao||Guangzhi Wang||Shanghang Zhang||Yang Gu||Yaxian Li||Zhichao Song||Pengfei Xu||Runbo Hu||Hua Chai||Kurt Keutzer. "Multi-Source Distilling Domain Adaptation". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 12975-12983.

Sicheng Zhao||Guangzhi Wang||Shanghang Zhang||Yang Gu||Yaxian Li||Zhichao Song||Pengfei Xu||Runbo Hu||Hua Chai||Kurt Keutzer. Multi-Source Distilling Domain Adaptation. AAAI[Internet]. 2020[cited 2023]; 12975-12983.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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